Library & Dataset

Using OLR

Inspect Dataset Using Training and Validation

OLR Equations

Using all variables

Inspect Dataset Using Training and Validation

Training set divide in the Diagnostic_based.on.Harell.Rmd file

vclust <- varclus (~angle+brick+wood+mixed+ density+EN +TC + TC_mature_soil + TC_saprolite_soil +  TC_weath_rock  + TC_unstable_structure  + T_construction  + spring +  landfill + garbage  + crack + leaning_wall + scars + downward_floor + tilted + fracture + conc_rainfall + wastewater + leak + septic_tank  + tree + ground_veg + deforestation + banana + drainage , data=train.data)

# took out density since training has 0 d4 and it was not allowing do the plot

p <- plot(vclust)

par(mfrow=c(6,5))
plot.xmean.ordinaly (risk~angle+brick+wood+mixed+ density+EN +TC + TC_mature_soil + TC_saprolite_soil +  TC_weath_rock  + TC_unstable_structure  + T_construction  + spring +  landfill + garbage  + crack + leaning_wall + scars + downward_floor + tilted + fracture + conc_rainfall + wastewater + leak + septic_tank  + tree + ground_veg + deforestation + banana + drainage, data=train.data, cr=TRUE , subn=FALSE)

#angle + building+density+EN +TC + TC_mature_Soil + TC_saprolito +  TC_weath_rock + TC_rock + TC_geol_desfav + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + DepTaludeAterro + aterro + lixo + entulho + crack + belly_wall + scars + drawback + tilted + fracture + conc_rainfall_water + wastewater + leak + septic_tank + drainage + tree + ground_veg + deforestation + banana

Diagnostic 2: Proportion (-5% of one of the parameters based on what is expected. Since some parameters have 2 predictors, others 5)

#library(plyr)
brick <- count(train.data$brick) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "brick")

wood <- count(train.data$wood) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "wood")

mixed <- count(train.data$mixed) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "mixed")

TC_mature_soil <- count(train.data$TC_mature_soil) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_mature_soil")

T_construction  <- count(train.data$T_construction ) %>%
  mutate ("Percentage"=(freq/265)*100) %>%
  mutate("Classifier" = "T_construction ")

spring <- count(train.data$spring) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "spring")

landfill <- count(train.data$landfill) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "landfill")

garbage <- count(train.data$garbage) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "garbage")

crack <- count(train.data$crack) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "crack")

leaning_wall <- count(train.data$leaning_wall) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "leaning_wall")

scars <- count(train.data$scars) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "DepTaludeAterro")

downward_floor <- count(train.data$downward_floor) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "scars")

tilted <- count(train.data$tilted) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "tilted")

conc_rainfall <- count(train.data$conc_rainfall) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "conc_rainfall")

wastewater <- count(train.data$wastewater) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "wastewater")

leak <- count(train.data$leak) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "conc_rainfall_water")

septic_tank <- count(train.data$septic_tank) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "septic_tank")

angle <- count(train.data$angle) # angle A less than 5% but the rest are okay (3,50, 91, 277, 109) Expected=106
angle <- angle %>%
  mutate("Percentage"=(freq/106)*100)%>%
  mutate("Classifier" = "angle")

EN <- count(train.data$EN) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "EN")

TC <- count(train.data$TC)  %>%
  mutate ("Percentage"=(freq/265)*100) %>%
  mutate("Classifier" = "TC")

TC_saprolite_soil  <- count(train.data$TC_saprolite_soil )  %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_saprolite_soil ")

banana <- count(train.data$banana) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "banana")

drainage <- count(train.data$drainage) %>%
  mutate ("Percentage"=(freq/176.7)*100)%>%
  mutate("Classifier" = "drainage")

deforestation <- count(train.data$deforestation) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "deforestation")

TC_unstable_structure  <- count(train.data$TC_unstable_structure ) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_unstable_structure ")


tree <- count(train.data$tree) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "tree")

ground_veg <- count(train.data$ground_veg) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "ground_veg")


density <- count(train.data$density)  %>% #(79, 415, 36) # d4 =0 
  mutate ("Percentage"=(freq/132.5)*100)%>%
  mutate("Classifier" = "density")

TC_weath_rock  <- count(train.data$TC_weath_rock ) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_weath_rock ")

fracture <- count(train.data$fracture) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "fracture")









df <- rbind(brick, wood, mixed, TC_mature_soil, T_construction, spring, landfill, garbage, crack, leaning_wall, scars, downward_floor, tilted, conc_rainfall, wastewater, leak, septic_tank, angle, EN, TC, TC_saprolite_soil,  banana, drainage, deforestation, TC_unstable_structure, tree, ground_veg,density, TC_weath_rock, fracture)

df

##        x freq Percentage             Classifier
## 1  FALSE   31  11.698113                  brick
## 2   TRUE  499 188.301887                  brick
## 3  FALSE  452 170.566038                   wood
## 4   TRUE   78  29.433962                   wood
## 5  FALSE  493 186.037736                  mixed
## 6   TRUE   37  13.962264                  mixed
## 7  FALSE  246  92.830189         TC_mature_soil
## 8   TRUE  284 107.169811         TC_mature_soil
## 9  FALSE  213  80.377358        T_construction 
## 10  TRUE  317 119.622642        T_construction 
## 11 FALSE  513 193.584906                 spring
## 12  TRUE   17   6.415094                 spring
## 13 FALSE  331 124.905660               landfill
## 14  TRUE  199  75.094340               landfill
## 15 FALSE  345 130.188679                garbage
## 16  TRUE  185  69.811321                garbage
## 17 FALSE  436 164.528302                  crack
## 18  TRUE   94  35.471698                  crack
## 19 FALSE  498 187.924528           leaning_wall
## 20  TRUE   32  12.075472           leaning_wall
## 21 FALSE  328 123.773585        DepTaludeAterro
## 22  TRUE  202  76.226415        DepTaludeAterro
## 23 FALSE  467 176.226415                  scars
## 24  TRUE   63  23.773585                  scars
## 25 FALSE  437 164.905660                 tilted
## 26  TRUE   93  35.094340                 tilted
## 27 FALSE   12   4.528302          conc_rainfall
## 28  TRUE  518 195.471698          conc_rainfall
## 29 FALSE  204  76.981132             wastewater
## 30  TRUE  326 123.018868             wastewater
## 31 FALSE  344 129.811321    conc_rainfall_water
## 32  TRUE  186  70.188679    conc_rainfall_water
## 33 FALSE  525 198.113208            septic_tank
## 34  TRUE    5   1.886792            septic_tank
## 35     C   32  30.188679                  angle
## 36     D  119 112.264151                  angle
## 37     E  379 357.547170                  angle
## 38 FALSE  347 130.943396                     EN
## 39  TRUE  183  69.056604                     EN
## 40 FALSE   24   9.056604                     TC
## 41  TRUE  506 190.943396                     TC
## 42 FALSE  441 166.415094     TC_saprolite_soil 
## 43  TRUE   89  33.584906     TC_saprolite_soil 
## 44 FALSE  349 131.698113                 banana
## 45  TRUE  181  68.301887                 banana
## 46     Y   64  36.219581               drainage
## 47     P  236 133.559706               drainage
## 48     N  230 130.164120               drainage
## 49 FALSE  496 187.169811          deforestation
## 50  TRUE   34  12.830189          deforestation
## 51 FALSE  519 195.849057 TC_unstable_structure 
## 52  TRUE   11   4.150943 TC_unstable_structure 
## 53 FALSE  207  78.113208                   tree
## 54  TRUE  323 121.886792                   tree
## 55 FALSE  156  58.867925             ground_veg
## 56  TRUE  374 141.132075             ground_veg
## 57    d1   69  52.075472                density
## 58    d2  425 320.754717                density
## 59    d3   36  27.169811                density
## 60 FALSE  518 195.471698         TC_weath_rock 
## 61  TRUE   12   4.528302         TC_weath_rock 
## 62 FALSE  528 199.245283               fracture
## 63  TRUE    2   0.754717               fracture

TC_weath_rock, TC_rock_TC_geol_desf, fracture, TC_rock

OF: angle (slope angle), building (building material), EN (natural slope), TC_saprolito (cut slope with saprolite), Taterro (slope with landfill), DepEncostaNatural (natural slope with construction material), DepTaludeAterro (slope with landfill and construction material), DepTaludeCorte (cut slope with construction material), landfill, garbage, construction_deposit (construction material), crack, leaning_wall, scars, downward_floor, tilted, conc_rainfall_water (concentration of rainfall water), wastewater, leak, drainage
MOF:TC_mature_soil (slope cut with soil), density, TC (slope cut), - - TC_geol_desfav (cut slope with geology favorable to instability), tree, ground_veg (ground vegetation), deforestation, banana
NOF: TC_weath_rock (cut slope with weathering rock), TC_rock (cut slope in bedrock), fracture, septic_tank
GP:all classifiers minus BP classifiers
BP: TC_weath_rock (True N= 11 , False N= 529), TC_geol_desf (True N= 11 , False N= 529), fracture (True N=1 , False N=529), TC_rock (True N=1 , False N=529 ), septic_tank (True N=4 , False N=526 )

Equation 1

consider all parameter okay from first diagnostic:

f1 <- lrm(risk ~ building + EN + TC_saprolito + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + landfill + garbage + construction_deposit + crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + leak + tree + ground_veg + banana , data=train.data, x=TRUE , y=TRUE)

# Equation 1

eq_OLR_01 <- polr(risk ~ brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil, data= train.data
           ,  method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_01))



p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                               Value Std. Error     t value      p value
## brickTRUE             -0.6018733932  0.4872512 -1.23524245 1.083701e-01
## woodTRUE               1.0656776588  0.3362246  3.16954139 7.633986e-04
## ENTRUE                 1.0997471170  0.3694436  2.97676612 1.456531e-03
## TC_mature_soilTRUE     0.4815750531  0.2276160  2.11573455 1.718370e-02
## T_constructionTRUE     0.3318448911  0.3648991  0.90941556 1.815654e-01
## springTRUE            -0.8185097624  0.6849166 -1.19505030 1.160337e-01
## landfillTRUE           0.1533903325  0.3289735  0.46626950 3.205113e-01
## leakTRUE               0.0004770532  0.2417038  0.00197371 4.992126e-01
## garbageTRUE            0.0710703108  0.2982016  0.23832974 4.058127e-01
## crackTRUE              1.9256460052  0.3445374  5.58907742 1.141396e-08
## leaning_wallTRUE       1.9860854014  0.5445733  3.64704850 1.326349e-04
## scarsTRUE              4.1183626917  0.3999813 10.29638909 3.657799e-25
## downward_floorTRUE     1.1296938425  0.3851397  2.93320528 1.677410e-03
## tiltedTRUE             1.1970379105  0.3447847  3.47184178 2.584504e-04
## septic_tankTRUE        1.3602767553  1.0310445  1.31931919 9.353121e-02
## conc_rainfallTRUE      2.4958976948  0.7630364  3.27100745 5.358254e-04
## wastewaterTRUE         0.9940218278  0.2471774  4.02149156 2.891539e-05
## ground_vegTRUE         1.1069885289  0.2683566  4.12506473 1.853153e-05
## angleD                 0.8548078524  0.4882809  1.75064785 4.000329e-02
## angleE                 1.2593718860  0.5493167  2.29261535 1.093508e-02
## TC_saprolite_soilTRUE -0.0124332038  0.2920369 -0.04257408 4.830205e-01
## R1|R2                  2.7652996508  1.0594631  2.61009521 4.525851e-03
## R2|R3                  7.1722928548  1.1162619  6.42527758 6.581455e-11
## R3|R4                 12.8357693844  1.2659224 10.13945949 1.845682e-24

stargazer((ctable), type="text", style="default", digits = 2)

## 
## =======================================================
##                       Value  Std. Error t value p value
## -------------------------------------------------------
## brickTRUE             -0.60     0.49     -1.24   0.11  
## woodTRUE               1.07     0.34     3.17    0.001 
## ENTRUE                 1.10     0.37     2.98    0.001 
## TC_mature_soilTRUE     0.48     0.23     2.12    0.02  
## T_constructionTRUE     0.33     0.36     0.91    0.18  
## springTRUE            -0.82     0.68     -1.20   0.12  
## landfillTRUE           0.15     0.33     0.47    0.32  
## leakTRUE              0.0005    0.24     0.002   0.50  
## garbageTRUE            0.07     0.30     0.24    0.41  
## crackTRUE              1.93     0.34     5.59      0   
## leaning_wallTRUE       1.99     0.54     3.65   0.0001 
## scarsTRUE              4.12     0.40     10.30     0   
## downward_floorTRUE     1.13     0.39     2.93    0.002 
## tiltedTRUE             1.20     0.34     3.47   0.0003 
## septic_tankTRUE        1.36     1.03     1.32    0.09  
## conc_rainfallTRUE      2.50     0.76     3.27    0.001 
## wastewaterTRUE         0.99     0.25     4.02   0.0000 
## ground_vegTRUE         1.11     0.27     4.13   0.0000 
## angleD                 0.85     0.49     1.75    0.04  
## angleE                 1.26     0.55     2.29    0.01  
## TC_saprolite_soilTRUE -0.01     0.29     -0.04   0.48  
## R1| R2                 2.77     1.06     2.61    0.005 
## R2| R3                 7.17     1.12     6.43      0   
## R3| R4                12.84     1.27     10.14     0   
## -------------------------------------------------------

less p-value = 0.10 (TC_saprolitoTRUE,TaterroTRUE, DepTaludeAterroTRUE,DepTaludeAterroTRUE,landfillTRUE, construction_depositTRUE, leakTRUE)

par(mfrow=c(5,4))
plot.xmean.ordinaly (risk~ brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil
          ,data=train.data, cr=TRUE , subn=FALSE ,  cex.lab=1.5, cex.axis=2, cex.sub=2, cex.main=2)

Creating function with four level

Equation 1

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +-----------------+---+---+----+---------+------------+----------+
## |                 |   |N  |y>=1|y>=2     |y>=3        |y>=4      |
## +-----------------+---+---+----+---------+------------+----------+
## |brick            |No | 31|Inf | 3.401197| 1.232143681|-0.8938179|
## |                 |Yes|498|Inf | 2.285041|-0.072320662|-2.0069620|
## +-----------------+---+---+----+---------+------------+----------+
## |wood             |No |451|Inf | 2.199691|-0.164449432|-2.3025851|
## |                 |Yes| 78|Inf | 3.637586| 0.998528830|-0.6359888|
## +-----------------+---+---+----+---------+------------+----------+
## |EN               |No |346|Inf | 1.850296|-0.483174092|-2.4693542|
## |                 |Yes|183|Inf |      Inf| 0.950976290|-1.2422550|
## +-----------------+---+---+----+---------+------------+----------+
## |TC_mature_soil   |No |246|Inf | 1.864785|-0.328115642|-2.0522906|
## |                 |Yes|283|Inf | 2.955654| 0.277383320|-1.8041820|
## +-----------------+---+---+----+---------+------------+----------+
## |T_construction   |No |213|Inf | 1.559566|-0.890145452|-3.1208954|
## |                 |Yes|316|Inf | 3.421000| 0.572069249|-1.4715386|
## +-----------------+---+---+----+---------+------------+----------+
## |spring           |No |512|Inf | 2.291890|-0.031252544|-2.0005935|
## |                 |Yes| 17|Inf |      Inf| 0.875468737|-0.3566749|
## +-----------------+---+---+----+---------+------------+----------+
## |landfill         |No |330|Inf | 1.820333|-0.520534438|-2.6888188|
## |                 |Yes|199|Inf | 5.288267| 0.888316880|-1.1737329|
## +-----------------+---+---+----+---------+------------+----------+
## |leak             |No |343|Inf | 1.942582|-0.335472736|-2.4203681|
## |                 |Yes|186|Inf | 3.817712| 0.621403276|-1.2947272|
## +-----------------+---+---+----+---------+------------+----------+
## |garbage          |No |345|Inf | 2.089262|-0.250578322|-2.3154058|
## |                 |Yes|184|Inf | 2.967561| 0.464707942|-1.3795147|
## +-----------------+---+---+----+---------+------------+----------+
## |crack            |No |435|Inf | 2.134938|-0.386344067|-2.7972813|
## |                 |Yes| 94|Inf | 4.532599| 2.685577345|-0.1706255|
## +-----------------+---+---+----+---------+------------+----------+
## |leaning_wall     |No |497|Inf | 2.259100|-0.124910650|-2.1684933|
## |                 |Yes| 32|Inf |      Inf| 3.433987204| 0.1251631|
## +-----------------+---+---+----+---------+------------+----------+
## |scars            |No |327|Inf | 1.784642|-1.337320357|-4.6821312|
## |                 |Yes|202|Inf |      Inf| 3.486355190|-0.7455937|
## +-----------------+---+---+----+---------+------------+----------+
## |downward_floor   |No |466|Inf | 2.187723|-0.267681406|-2.3120638|
## |                 |Yes| 63|Inf |      Inf| 4.127134385|-0.3528214|
## +-----------------+---+---+----+---------+------------+----------+
## |tilted           |No |436|Inf | 2.113432|-0.399734433|-2.5698999|
## |                 |Yes| 93|Inf |      Inf| 3.102342009|-0.4144338|
## +-----------------+---+---+----+---------+------------+----------+
## |septic_tank      |No |524|Inf | 2.317369|-0.015267472|-1.9372144|
## |                 |Yes|  5|Inf |      Inf| 1.386294361|-0.4054651|
## +-----------------+---+---+----+---------+------------+----------+
## |conc_rainfall    |No | 12|Inf |-1.098612|-2.397895273|      -Inf|
## |                 |Yes|517|Inf | 2.534114| 0.034819765|-1.8875152|
## +-----------------+---+---+----+---------+------------+----------+
## |wastewater       |No |204|Inf | 1.574551|-0.417735201|-3.0757750|
## |                 |Yes|325|Inf | 3.261297| 0.253659095|-1.5059589|
## +-----------------+---+---+----+---------+------------+----------+
## |ground_veg       |No |156|Inf | 1.203973|-1.519825754|-2.9177707|
## |                 |Yes|373|Inf | 3.493749| 0.543850879|-1.6518586|
## +-----------------+---+---+----+---------+------------+----------+
## |angle            |C  | 32|Inf |      Inf|-0.251314428|-3.4339872|
## |                 |D  |119|Inf | 4.770685| 1.000172216|-1.2259517|
## |                 |E  |378|Inf | 1.976494|-0.276887827|-2.1341664|
## +-----------------+---+---+----+---------+------------+----------+
## |TC_saprolite_soil|No |440|Inf | 2.222736|-0.063657852|-1.9881328|
## |                 |Yes| 89|Inf | 3.056357| 0.294239473|-1.5960149|
## +-----------------+---+---+----+---------+------------+----------+
## |Overall          |   |529|Inf | 2.327797|-0.003780723|-1.9138903|
## +-----------------+---+---+----+---------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=1, cex.axis=1, cex.sub=1)

Equation 2

parameters okay and so/so
porportion
excluded TC_geol_desf

f2 <- lrm(risk ~ angle + building + EN + TC_saprolito + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + landfill + garbage + construction_deposit + crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + leak + drainage + TC_mature_Soil + density + TC + tree +ground_veg + deforestation + banana , data=train.data, x=TRUE , y=TRUE)

      stargazer(anova(f2), type="text", style="default")

eq_OLR_02 <- polr(risk ~ brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation,
                  
                 data= train.data
           ,  method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_02))








p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                             Value Std. Error     t value      p value
## brickTRUE             -0.35495918  0.5336091 -0.66520447 2.529599e-01
## woodTRUE               0.86438238  0.3532877  2.44668142 7.208911e-03
## ENTRUE                 0.94074660  0.3901683  2.41113042 7.951580e-03
## TC_mature_soilTRUE     0.41739746  0.2408280  1.73317649 4.153215e-02
## T_constructionTRUE     0.45321136  0.3763691  1.20416722 1.142625e-01
## landfillTRUE           0.10904467  0.3342528  0.32623417 3.721236e-01
## leakTRUE              -0.15857083  0.2479088 -0.63963367 2.612054e-01
## garbageTRUE            0.07052007  0.3062060  0.23030273 4.089283e-01
## crackTRUE              1.89848152  0.3466747  5.47626270 2.172013e-08
## leaning_wallTRUE       1.99189222  0.5551940  3.58774095 1.667777e-04
## treeTRUE              -0.10217337  0.2478906 -0.41217125 3.401070e-01
## downward_floorTRUE     1.02613575  0.3812123  2.69177014 3.553696e-03
## tiltedTRUE             0.95345086  0.3415403  2.79162019 2.622244e-03
## ground_vegTRUE         1.00521050  0.2886202  3.48281417 2.480863e-04
## scarsTRUE              4.05569868  0.4022152 10.08340445 3.270967e-24
## mixedTRUE              0.29482505  0.5088802  0.57936043 2.811730e-01
## conc_rainfallTRUE      1.96259700  0.7813614  2.51176591 6.006437e-03
## wastewaterTRUE         0.81439888  0.2539654  3.20673147 6.712612e-04
## angleD                 0.70185027  0.5033650  1.39431690 8.161096e-02
## angleE                 1.13347791  0.5605856  2.02195340 2.159058e-02
## bananaTRUE             0.17111166  0.2630963  0.65037665 2.577245e-01
## drainage.L             1.13495754  0.2900471  3.91301076 4.557624e-05
## drainage.Q             0.04428922  0.1931407  0.22931062 4.093138e-01
## TC_saprolite_soilTRUE -0.01056727  0.3000107 -0.03522297 4.859510e-01
## TCTRUE                -0.41159268  0.5848452 -0.70376345 2.407900e-01
## deforestationTRUE      0.43622231  0.4313084  1.01139296 1.559142e-01
## R1|R2                  1.69307012  1.3186446  1.28394726 9.958021e-02
## R2|R3                  6.35997085  1.3494722  4.71293218 1.220887e-06
## R3|R4                 12.00955718  1.4790896  8.11956041 2.339376e-16

stargazer((ctable), type="text", style="default", digits=2)

## 
## ======================================================
##                       Value Std. Error t value p value
## ------------------------------------------------------
## brickTRUE             -0.35    0.53     -0.67   0.25  
## woodTRUE              0.86     0.35     2.45    0.01  
## ENTRUE                0.94     0.39     2.41    0.01  
## TC_mature_soilTRUE    0.42     0.24     1.73    0.04  
## T_constructionTRUE    0.45     0.38     1.20    0.11  
## landfillTRUE          0.11     0.33     0.33    0.37  
## leakTRUE              -0.16    0.25     -0.64   0.26  
## garbageTRUE           0.07     0.31     0.23    0.41  
## crackTRUE             1.90     0.35     5.48   0.0000 
## leaning_wallTRUE      1.99     0.56     3.59   0.0002 
## treeTRUE              -0.10    0.25     -0.41   0.34  
## downward_floorTRUE    1.03     0.38     2.69    0.004 
## tiltedTRUE            0.95     0.34     2.79    0.003 
## ground_vegTRUE        1.01     0.29     3.48   0.0002 
## scarsTRUE             4.06     0.40     10.08     0   
## mixedTRUE             0.29     0.51     0.58    0.28  
## conc_rainfallTRUE     1.96     0.78     2.51    0.01  
## wastewaterTRUE        0.81     0.25     3.21    0.001 
## angleD                0.70     0.50     1.39    0.08  
## angleE                1.13     0.56     2.02    0.02  
## bananaTRUE            0.17     0.26     0.65    0.26  
## drainage.L            1.13     0.29     3.91   0.0000 
## drainage.Q            0.04     0.19     0.23    0.41  
## TC_saprolite_soilTRUE -0.01    0.30     -0.04   0.49  
## TCTRUE                -0.41    0.58     -0.70   0.24  
## deforestationTRUE     0.44     0.43     1.01    0.16  
## R1| R2                1.69     1.32     1.28    0.10  
## R2| R3                6.36     1.35     4.71   0.0000 
## R3| R4                12.01    1.48     8.12      0   
## ------------------------------------------------------

par(mfrow=c(6,4))
plot.xmean.ordinaly (risk~ brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation
          ,data=train.data, cr=TRUE , subn=FALSE ,  cex.lab=1.5, cex.axis=4, cex.sub=4, cex.main=4)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation,data=train.data
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +-----------------+---+---+----+----------+------------+----------+
## |                 |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +-----------------+---+---+----+----------+------------+----------+
## |brick            |No | 31|Inf | 3.4011974| 1.232143681|-0.8938179|
## |                 |Yes|498|Inf | 2.2850408|-0.072320662|-2.0069620|
## +-----------------+---+---+----+----------+------------+----------+
## |wood             |No |451|Inf | 2.1996907|-0.164449432|-2.3025851|
## |                 |Yes| 78|Inf | 3.6375862| 0.998528830|-0.6359888|
## +-----------------+---+---+----+----------+------------+----------+
## |EN               |No |346|Inf | 1.8502960|-0.483174092|-2.4693542|
## |                 |Yes|183|Inf |       Inf| 0.950976290|-1.2422550|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_mature_soil   |No |246|Inf | 1.8647846|-0.328115642|-2.0522906|
## |                 |Yes|283|Inf | 2.9556540| 0.277383320|-1.8041820|
## +-----------------+---+---+----+----------+------------+----------+
## |T_construction   |No |213|Inf | 1.5595661|-0.890145452|-3.1208954|
## |                 |Yes|316|Inf | 3.4210000| 0.572069249|-1.4715386|
## +-----------------+---+---+----+----------+------------+----------+
## |landfill         |No |330|Inf | 1.8203328|-0.520534438|-2.6888188|
## |                 |Yes|199|Inf | 5.2882670| 0.888316880|-1.1737329|
## +-----------------+---+---+----+----------+------------+----------+
## |leak             |No |343|Inf | 1.9425824|-0.335472736|-2.4203681|
## |                 |Yes|186|Inf | 3.8177123| 0.621403276|-1.2947272|
## +-----------------+---+---+----+----------+------------+----------+
## |garbage          |No |345|Inf | 2.0892616|-0.250578322|-2.3154058|
## |                 |Yes|184|Inf | 2.9675614| 0.464707942|-1.3795147|
## +-----------------+---+---+----+----------+------------+----------+
## |crack            |No |435|Inf | 2.1349379|-0.386344067|-2.7972813|
## |                 |Yes| 94|Inf | 4.5325995| 2.685577345|-0.1706255|
## +-----------------+---+---+----+----------+------------+----------+
## |leaning_wall     |No |497|Inf | 2.2591000|-0.124910650|-2.1684933|
## |                 |Yes| 32|Inf |       Inf| 3.433987204| 0.1251631|
## +-----------------+---+---+----+----------+------------+----------+
## |tree             |No |206|Inf | 1.6933194|-0.536801110|-2.1758334|
## |                 |Yes|323|Inf | 3.0220496| 0.331167184|-1.7702533|
## +-----------------+---+---+----+----------+------------+----------+
## |downward_floor   |No |466|Inf | 2.1877233|-0.267681406|-2.3120638|
## |                 |Yes| 63|Inf |       Inf| 4.127134385|-0.3528214|
## +-----------------+---+---+----+----------+------------+----------+
## |tilted           |No |436|Inf | 2.1134317|-0.399734433|-2.5698999|
## |                 |Yes| 93|Inf |       Inf| 3.102342009|-0.4144338|
## +-----------------+---+---+----+----------+------------+----------+
## |ground_veg       |No |156|Inf | 1.2039728|-1.519825754|-2.9177707|
## |                 |Yes|373|Inf | 3.4937489| 0.543850879|-1.6518586|
## +-----------------+---+---+----+----------+------------+----------+
## |scars            |No |327|Inf | 1.7846420|-1.337320357|-4.6821312|
## |                 |Yes|202|Inf |       Inf| 3.486355190|-0.7455937|
## +-----------------+---+---+----+----------+------------+----------+
## |mixed            |No |492|Inf | 2.2716776|-0.089490571|-2.0126015|
## |                 |Yes| 37|Inf | 3.5835189| 1.287854288|-0.9932518|
## +-----------------+---+---+----+----------+------------+----------+
## |conc_rainfall    |No | 12|Inf |-1.0986123|-2.397895273|      -Inf|
## |                 |Yes|517|Inf | 2.5341144| 0.034819765|-1.8875152|
## +-----------------+---+---+----+----------+------------+----------+
## |wastewater       |No |204|Inf | 1.5745507|-0.417735201|-3.0757750|
## |                 |Yes|325|Inf | 3.2612965| 0.253659095|-1.5059589|
## +-----------------+---+---+----+----------+------------+----------+
## |angle            |C  | 32|Inf |       Inf|-0.251314428|-3.4339872|
## |                 |D  |119|Inf | 4.7706846| 1.000172216|-1.2259517|
## |                 |E  |378|Inf | 1.9764936|-0.276887827|-2.1341664|
## +-----------------+---+---+----+----------+------------+----------+
## |banana           |No |348|Inf | 1.9328381|-0.383958903|-2.1908551|
## |                 |Yes|181|Inf | 4.0831713| 0.751741345|-1.5007047|
## +-----------------+---+---+----+----------+------------+----------+
## |drainage         |Y  | 64|Inf | 0.6466272|-1.686398954|      -Inf|
## |                 |P  |235|Inf | 2.3214536|-0.680408155|-2.7591054|
## |                 |N  |230|Inf | 4.0342406| 1.157452789|-1.1814999|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_saprolite_soil|No |440|Inf | 2.2227362|-0.063657852|-1.9881328|
## |                 |Yes| 89|Inf | 3.0563569| 0.294239473|-1.5960149|
## +-----------------+---+---+----+----------+------------+----------+
## |TC               |No | 24|Inf |       Inf| 1.098612289|-1.0986123|
## |                 |Yes|505|Inf | 2.2767216|-0.051496526|-1.9664354|
## +-----------------+---+---+----+----------+------------+----------+
## |deforestation    |No |495|Inf | 2.3025851| 0.028284714|-1.8893979|
## |                 |Yes| 34|Inf | 2.7725887|-0.479573080|-2.3353749|
## +-----------------+---+---+----+----------+------------+----------+
## |Overall          |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +-----------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=1, cex.axis=2, cex.sub=1)

Equation 3

parameters okay and so/so
porportion
p-value based equation 2 > 0.5

f3 <- lrm(risk ~ angle +building + EN + DepTaludeAterro+ DepTaludeCorte+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall_water+ wastewater+ tree + TC , data=train.data, x=TRUE , y=TRUE) stargazer(anova(f3), type=“text”, style=“default”)

# x=TRUE, y=TRUE used by resid() below 

eq_OLR_03 <- polr(risk ~ wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage, data=train.data
           ,  method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_03))


p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                          Value Std. Error    t value      p value
## woodTRUE            0.83135836  0.3311830  2.5102692 6.031958e-03
## TC_mature_soilTRUE  0.39939524  0.2277066  1.7539909 3.971603e-02
## T_constructionTRUE  0.42433259  0.2963313  1.4319532 7.607861e-02
## landfillTRUE        0.21270739  0.3001804  0.7085985 2.392868e-01
## crackTRUE           1.87359757  0.3341107  5.6077144 1.025080e-08
## leaning_wallTRUE    2.00789592  0.5515410  3.6405200 1.360440e-04
## treeTRUE           -0.06931478  0.2384102 -0.2907375 3.856260e-01
## downward_floorTRUE  0.96970117  0.3708572  2.6147565 4.464554e-03
## tiltedTRUE          1.02795041  0.3347492  3.0708075 1.067404e-03
## ground_vegTRUE      0.96912298  0.2813959  3.4439838 2.866051e-04
## scarsTRUE           4.04620548  0.3973464 10.1830690 1.179993e-24
## conc_rainfallTRUE   1.89437723  0.7549782  2.5091814 6.050566e-03
## wastewaterTRUE      0.81017630  0.2456282  3.2983849 4.862137e-04
## bananaTRUE          0.25335547  0.2482114  1.0207247 1.536925e-01
## drainage.L          1.18629284  0.2848233  4.1650128 1.556676e-05
## drainage.Q          0.03989566  0.1907864  0.2091116 4.171805e-01
## R1|R2               1.20693488  0.7431927  1.6239865 5.218934e-02
## R2|R3               5.76089623  0.7931696  7.2631326 1.891134e-13
## R3|R4              11.31771066  0.9556319 11.8431696 1.167300e-32

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## woodTRUE           0.83     0.33     2.51    0.01  
## TC_mature_soilTRUE 0.40     0.23     1.75    0.04  
## T_constructionTRUE 0.42     0.30     1.43    0.08  
## landfillTRUE       0.21     0.30     0.71    0.24  
## crackTRUE          1.87     0.33     5.61      0   
## leaning_wallTRUE   2.01     0.55     3.64   0.0001 
## treeTRUE           -0.07    0.24     -0.29   0.39  
## downward_floorTRUE 0.97     0.37     2.61    0.004 
## tiltedTRUE         1.03     0.33     3.07    0.001 
## ground_vegTRUE     0.97     0.28     3.44   0.0003 
## scarsTRUE          4.05     0.40     10.18     0   
## conc_rainfallTRUE  1.89     0.75     2.51    0.01  
## wastewaterTRUE     0.81     0.25     3.30   0.0005 
## bananaTRUE         0.25     0.25     1.02    0.15  
## drainage.L         1.19     0.28     4.17   0.0000 
## drainage.Q         0.04     0.19     0.21    0.42  
## R1| R2             1.21     0.74     1.62    0.05  
## R2| R3             5.76     0.79     7.26      0   
## R3| R4             11.32    0.96     11.84     0   
## ---------------------------------------------------

#print (f3 , latex =TRUE , coefs =5)

par(mfrow=c(3,5))
plot.xmean.ordinaly (risk ~  wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage,,
          data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+----------+------------+----------+
## |              |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +--------------+---+---+----+----------+------------+----------+
## |wood          |No |451|Inf | 2.1996907|-0.164449432|-2.3025851|
## |              |Yes| 78|Inf | 3.6375862| 0.998528830|-0.6359888|
## +--------------+---+---+----+----------+------------+----------+
## |TC_mature_soil|No |246|Inf | 1.8647846|-0.328115642|-2.0522906|
## |              |Yes|283|Inf | 2.9556540| 0.277383320|-1.8041820|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |213|Inf | 1.5595661|-0.890145452|-3.1208954|
## |              |Yes|316|Inf | 3.4210000| 0.572069249|-1.4715386|
## +--------------+---+---+----+----------+------------+----------+
## |landfill      |No |330|Inf | 1.8203328|-0.520534438|-2.6888188|
## |              |Yes|199|Inf | 5.2882670| 0.888316880|-1.1737329|
## +--------------+---+---+----+----------+------------+----------+
## |crack         |No |435|Inf | 2.1349379|-0.386344067|-2.7972813|
## |              |Yes| 94|Inf | 4.5325995| 2.685577345|-0.1706255|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.2591000|-0.124910650|-2.1684933|
## |              |Yes| 32|Inf |       Inf| 3.433987204| 0.1251631|
## +--------------+---+---+----+----------+------------+----------+
## |tree          |No |206|Inf | 1.6933194|-0.536801110|-2.1758334|
## |              |Yes|323|Inf | 3.0220496| 0.331167184|-1.7702533|
## +--------------+---+---+----+----------+------------+----------+
## |downward_floor|No |466|Inf | 2.1877233|-0.267681406|-2.3120638|
## |              |Yes| 63|Inf |       Inf| 4.127134385|-0.3528214|
## +--------------+---+---+----+----------+------------+----------+
## |tilted        |No |436|Inf | 2.1134317|-0.399734433|-2.5698999|
## |              |Yes| 93|Inf |       Inf| 3.102342009|-0.4144338|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg    |No |156|Inf | 1.2039728|-1.519825754|-2.9177707|
## |              |Yes|373|Inf | 3.4937489| 0.543850879|-1.6518586|
## +--------------+---+---+----+----------+------------+----------+
## |scars         |No |327|Inf | 1.7846420|-1.337320357|-4.6821312|
## |              |Yes|202|Inf |       Inf| 3.486355190|-0.7455937|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 12|Inf |-1.0986123|-2.397895273|      -Inf|
## |              |Yes|517|Inf | 2.5341144| 0.034819765|-1.8875152|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater    |No |204|Inf | 1.5745507|-0.417735201|-3.0757750|
## |              |Yes|325|Inf | 3.2612965| 0.253659095|-1.5059589|
## +--------------+---+---+----+----------+------------+----------+
## |banana        |No |348|Inf | 1.9328381|-0.383958903|-2.1908551|
## |              |Yes|181|Inf | 4.0831713| 0.751741345|-1.5007047|
## +--------------+---+---+----+----------+------------+----------+
## |drainage      |Y  | 64|Inf | 0.6466272|-1.686398954|      -Inf|
## |              |P  |235|Inf | 2.3214536|-0.680408155|-2.7591054|
## |              |N  |230|Inf | 4.0342406| 1.157452789|-1.1814999|
## +--------------+---+---+----+----------+------------+----------+
## |Overall       |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.6, cex.axis=0.6, cex.sub=0.6)

Equation 4

p-value equation 3 > 0.05 (banana, DepTaludeCorte)
consider proportion
paremeters okay & so/so
fashion order

f4 <- lrm(risk ~ building + EN
+ DepEncNatural
+ crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + drainage + TC_mature_Soil + TC + +ground_veg
,data=train.data, x=TRUE , y=TRUE) # x=TRUE, y=TRUE used by resid() below #print (f4 , latex =TRUE , coefs =5) stargazer(anova(f4), type=“text”, style=“default”)

eq_OLR_04 <- polr(risk~ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
                  , data= train.data
           ,  method = "logistic", Hess = TRUE)
p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value

ctable <- coef(summary(eq_OLR_04))

ctable <- cbind(ctable, "p value" = p )

## Warning in cbind(ctable, `p value` = p): number of rows of result is not a
## multiple of vector length (arg 2)

ctable

##                          Value Std. Error    t value      p value
## woodTRUE            0.83393744  0.3306662  2.5219917 6.031958e-03
## TC_mature_soilTRUE  0.37613377  0.2251925  1.6702768 3.971603e-02
## T_constructionTRUE  0.55082418  0.2365747  2.3283307 7.607861e-02
## crackTRUE           1.89755683  0.3325553  5.7059887 2.392868e-01
## leaning_wallTRUE    1.99722089  0.5533274  3.6094740 1.025080e-08
## treeTRUE           -0.08071835  0.2378132 -0.3394191 1.360440e-04
## downward_floorTRUE  0.99528575  0.3691033  2.6964964 3.856260e-01
## tiltedTRUE          1.06270681  0.3308423  3.2121246 4.464554e-03
## ground_vegTRUE      0.98013776  0.2806946  3.4918301 1.067404e-03
## scarsTRUE           4.04228874  0.3974688 10.1700786 2.866051e-04
## conc_rainfallTRUE   1.92878772  0.7560841  2.5510228 1.179993e-24
## wastewaterTRUE      0.78152454  0.2423022  3.2254122 6.050566e-03
## bananaTRUE          0.25426072  0.2481501  1.0246247 4.862137e-04
## drainage.L          1.19379934  0.2845278  4.1957217 1.536925e-01
## drainage.Q          0.04469379  0.1906217  0.2344632 1.556676e-05
## R1|R2               1.22366684  0.7454736  1.6414624 4.171805e-01
## R2|R3               5.77527951  0.7954518  7.2603764 5.218934e-02
## R3|R4              11.32476847  0.9576886 11.8251052 1.891134e-13

stargazer((ctable), type="text", style="default", digits=2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## woodTRUE           0.83     0.33     2.52    0.01  
## TC_mature_soilTRUE 0.38     0.23     1.67    0.04  
## T_constructionTRUE 0.55     0.24     2.33    0.08  
## crackTRUE          1.90     0.33     5.71    0.24  
## leaning_wallTRUE   2.00     0.55     3.61      0   
## treeTRUE           -0.08    0.24     -0.34  0.0001 
## downward_floorTRUE 1.00     0.37     2.70    0.39  
## tiltedTRUE         1.06     0.33     3.21    0.004 
## ground_vegTRUE     0.98     0.28     3.49    0.001 
## scarsTRUE          4.04     0.40     10.17  0.0003 
## conc_rainfallTRUE  1.93     0.76     2.55      0   
## wastewaterTRUE     0.78     0.24     3.23    0.01  
## bananaTRUE         0.25     0.25     1.02   0.0005 
## drainage.L         1.19     0.28     4.20    0.15  
## drainage.Q         0.04     0.19     0.23   0.0000 
## R1| R2             1.22     0.75     1.64    0.42  
## R2| R3             5.78     0.80     7.26    0.05  
## R3| R4             11.32    0.96     11.83     0   
## ---------------------------------------------------

par(mfrow=c(4,4))
plot.xmean.ordinaly (risk ~  wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+----------+------------+----------+
## |              |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +--------------+---+---+----+----------+------------+----------+
## |wood          |No |451|Inf | 2.1996907|-0.164449432|-2.3025851|
## |              |Yes| 78|Inf | 3.6375862| 0.998528830|-0.6359888|
## +--------------+---+---+----+----------+------------+----------+
## |TC_mature_soil|No |246|Inf | 1.8647846|-0.328115642|-2.0522906|
## |              |Yes|283|Inf | 2.9556540| 0.277383320|-1.8041820|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |213|Inf | 1.5595661|-0.890145452|-3.1208954|
## |              |Yes|316|Inf | 3.4210000| 0.572069249|-1.4715386|
## +--------------+---+---+----+----------+------------+----------+
## |crack         |No |435|Inf | 2.1349379|-0.386344067|-2.7972813|
## |              |Yes| 94|Inf | 4.5325995| 2.685577345|-0.1706255|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.2591000|-0.124910650|-2.1684933|
## |              |Yes| 32|Inf |       Inf| 3.433987204| 0.1251631|
## +--------------+---+---+----+----------+------------+----------+
## |tree          |No |206|Inf | 1.6933194|-0.536801110|-2.1758334|
## |              |Yes|323|Inf | 3.0220496| 0.331167184|-1.7702533|
## +--------------+---+---+----+----------+------------+----------+
## |downward_floor|No |466|Inf | 2.1877233|-0.267681406|-2.3120638|
## |              |Yes| 63|Inf |       Inf| 4.127134385|-0.3528214|
## +--------------+---+---+----+----------+------------+----------+
## |tilted        |No |436|Inf | 2.1134317|-0.399734433|-2.5698999|
## |              |Yes| 93|Inf |       Inf| 3.102342009|-0.4144338|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg    |No |156|Inf | 1.2039728|-1.519825754|-2.9177707|
## |              |Yes|373|Inf | 3.4937489| 0.543850879|-1.6518586|
## +--------------+---+---+----+----------+------------+----------+
## |scars         |No |327|Inf | 1.7846420|-1.337320357|-4.6821312|
## |              |Yes|202|Inf |       Inf| 3.486355190|-0.7455937|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 12|Inf |-1.0986123|-2.397895273|      -Inf|
## |              |Yes|517|Inf | 2.5341144| 0.034819765|-1.8875152|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater    |No |204|Inf | 1.5745507|-0.417735201|-3.0757750|
## |              |Yes|325|Inf | 3.2612965| 0.253659095|-1.5059589|
## +--------------+---+---+----+----------+------------+----------+
## |banana        |No |348|Inf | 1.9328381|-0.383958903|-2.1908551|
## |              |Yes|181|Inf | 4.0831713| 0.751741345|-1.5007047|
## +--------------+---+---+----+----------+------------+----------+
## |drainage      |Y  | 64|Inf | 0.6466272|-1.686398954|      -Inf|
## |              |P  |235|Inf | 2.3214536|-0.680408155|-2.7591054|
## |              |N  |230|Inf | 4.0342406| 1.157452789|-1.1814999|
## +--------------+---+---+----+----------+------------+----------+
## |Overall       |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

Equation 5 - Based on Equation 1

based on Eq 1
less p-value > 0.10 (

# x=TRUE, y=TRUE used by resid() below 
#print (f1 , latex =TRUE , coefs =5)
#stargazer(anova(f1), type="text", style="default")

eq_OLR_05 <- polr(risk ~ brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg,  data= train.data
           ,  method = "logistic", Hess = TRUE)

ctable <- coef(summary(eq_OLR_05))

p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                         Value Std. Error    t value      p value
## brickTRUE          -0.4295501  0.4683847 -0.9170881 1.795482e-01
## woodTRUE            1.0378407  0.3266174  3.1775424 7.426449e-04
## TC_mature_soilTRUE  0.4538598  0.2198583  2.0643289 1.949327e-02
## T_constructionTRUE  0.4965043  0.2302329  2.1565311 1.552111e-02
## crackTRUE           1.8479539  0.3270532  5.6503163 8.007644e-09
## leaning_wallTRUE    1.8869560  0.5371937  3.5126175 2.218579e-04
## scarsTRUE           4.1572032  0.3971522 10.4675318 6.088841e-26
## downward_floorTRUE  1.1749503  0.3708120  3.1685878 7.659074e-04
## tiltedTRUE          1.2501260  0.3302682  3.7851844 7.679733e-05
## conc_rainfallTRUE   2.4228941  0.7349069  3.2968722 4.888398e-04
## wastewaterTRUE      0.9727841  0.2344552  4.1491252 1.668741e-05
## ground_vegTRUE      1.1690630  0.2553513  4.5782540 2.344366e-06
## R1|R2               1.5810660  0.8618157  1.8345757 3.328429e-02
## R2|R3               5.8742572  0.9142081  6.4255143 6.571219e-11
## R3|R4              11.3625068  1.0531004 10.7895760 1.927822e-27

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## brickTRUE          -0.43    0.47     -0.92   0.18  
## woodTRUE           1.04     0.33     3.18    0.001 
## TC_mature_soilTRUE 0.45     0.22     2.06    0.02  
## T_constructionTRUE 0.50     0.23     2.16    0.02  
## crackTRUE          1.85     0.33     5.65      0   
## leaning_wallTRUE   1.89     0.54     3.51   0.0002 
## scarsTRUE          4.16     0.40     10.47     0   
## downward_floorTRUE 1.17     0.37     3.17    0.001 
## tiltedTRUE         1.25     0.33     3.79   0.0001 
## conc_rainfallTRUE  2.42     0.73     3.30   0.0005 
## wastewaterTRUE     0.97     0.23     4.15   0.0000 
## ground_vegTRUE     1.17     0.26     4.58   0.0000 
## R1| R2             1.58     0.86     1.83    0.03  
## R2| R3             5.87     0.91     6.43      0   
## R3| R4             11.36    1.05     10.79     0   
## ---------------------------------------------------

par(mfrow=c(3,4))
plot.xmean.ordinaly (risk ~  brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+---------+------------+----------+
## |              |   |N  |y>=1|y>=2     |y>=3        |y>=4      |
## +--------------+---+---+----+---------+------------+----------+
## |brick         |No | 31|Inf | 3.401197| 1.232143681|-0.8938179|
## |              |Yes|498|Inf | 2.285041|-0.072320662|-2.0069620|
## +--------------+---+---+----+---------+------------+----------+
## |wood          |No |451|Inf | 2.199691|-0.164449432|-2.3025851|
## |              |Yes| 78|Inf | 3.637586| 0.998528830|-0.6359888|
## +--------------+---+---+----+---------+------------+----------+
## |TC_mature_soil|No |246|Inf | 1.864785|-0.328115642|-2.0522906|
## |              |Yes|283|Inf | 2.955654| 0.277383320|-1.8041820|
## +--------------+---+---+----+---------+------------+----------+
## |T_construction|No |213|Inf | 1.559566|-0.890145452|-3.1208954|
## |              |Yes|316|Inf | 3.421000| 0.572069249|-1.4715386|
## +--------------+---+---+----+---------+------------+----------+
## |crack         |No |435|Inf | 2.134938|-0.386344067|-2.7972813|
## |              |Yes| 94|Inf | 4.532599| 2.685577345|-0.1706255|
## +--------------+---+---+----+---------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.259100|-0.124910650|-2.1684933|
## |              |Yes| 32|Inf |      Inf| 3.433987204| 0.1251631|
## +--------------+---+---+----+---------+------------+----------+
## |scars         |No |327|Inf | 1.784642|-1.337320357|-4.6821312|
## |              |Yes|202|Inf |      Inf| 3.486355190|-0.7455937|
## +--------------+---+---+----+---------+------------+----------+
## |downward_floor|No |466|Inf | 2.187723|-0.267681406|-2.3120638|
## |              |Yes| 63|Inf |      Inf| 4.127134385|-0.3528214|
## +--------------+---+---+----+---------+------------+----------+
## |tilted        |No |436|Inf | 2.113432|-0.399734433|-2.5698999|
## |              |Yes| 93|Inf |      Inf| 3.102342009|-0.4144338|
## +--------------+---+---+----+---------+------------+----------+
## |conc_rainfall |No | 12|Inf |-1.098612|-2.397895273|      -Inf|
## |              |Yes|517|Inf | 2.534114| 0.034819765|-1.8875152|
## +--------------+---+---+----+---------+------------+----------+
## |wastewater    |No |204|Inf | 1.574551|-0.417735201|-3.0757750|
## |              |Yes|325|Inf | 3.261297| 0.253659095|-1.5059589|
## +--------------+---+---+----+---------+------------+----------+
## |ground_veg    |No |156|Inf | 1.203973|-1.519825754|-2.9177707|
## |              |Yes|373|Inf | 3.493749| 0.543850879|-1.6518586|
## +--------------+---+---+----+---------+------------+----------+
## |Overall       |   |529|Inf | 2.327797|-0.003780723|-1.9138903|
## +--------------+---+---+----+---------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

OLR Equation 6

# x=TRUE, y=TRUE used by resid() below 
#print (f1 , latex =TRUE , coefs =5)
#stargazer(anova(f1), type="text", style="default")

eq_OLR_06 <- polr(risk ~ brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana,  data= train.data
           ,  method = "logistic", Hess = TRUE)

ctable <- coef(summary(eq_OLR_06))

p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                          Value Std. Error    t value      p value
## brickTRUE          -0.53202164  0.5208014 -1.0215441 1.534984e-01
## woodTRUE            0.95846754  0.3294205  2.9095566 1.809709e-03
## mixedTRUE           0.54735955  0.4993175  1.0962154 1.364923e-01
## ENTRUE              1.05803433  0.3763199  2.8115292 2.465331e-03
## TCTRUE             -0.13671777  0.5469141 -0.2499803 4.013013e-01
## T_constructionTRUE  0.34072092  0.3602944  0.9456736 1.721575e-01
## landfillTRUE        0.20089756  0.3217457  0.6243985 2.661829e-01
## leakTRUE            0.05852226  0.2374896  0.2464203 4.026785e-01
## garbageTRUE         0.07488315  0.2954338  0.2534685 3.999531e-01
## crackTRUE           1.80726206  0.3356268  5.3847369 3.627534e-08
## leaning_wallTRUE    2.01826928  0.5475272  3.6861534 1.138346e-04
## treeTRUE           -0.05640807  0.2391328 -0.2358860 4.067606e-01
## tiltedTRUE          1.18959792  0.3343679  3.5577512 1.870217e-04
## angleD              0.84675859  0.4915621  1.7225874 4.248158e-02
## angleE              1.30374217  0.5444045  2.3948044 8.314617e-03
## ground_vegTRUE      1.08021836  0.2790457  3.8711158 5.416915e-05
## scarsTRUE           4.22468431  0.4011933 10.5302975 3.131809e-26
## conc_rainfallTRUE   2.73430638  0.7553445  3.6199459 1.473323e-04
## wastewaterTRUE      0.93190708  0.2403299  3.8776153 5.274266e-05
## bananaTRUE          0.21831583  0.2567834  0.8501945 1.976085e-01
## R1|R2               2.77094701  1.2624858  2.1948341 1.408775e-02
## R2|R3               7.07044977  1.3069745  5.4097839 3.155043e-08
## R3|R4              12.59307323  1.4371266  8.7626752 9.533520e-19

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## brickTRUE          -0.53    0.52     -1.02   0.15  
## woodTRUE           0.96     0.33     2.91    0.002 
## mixedTRUE          0.55     0.50     1.10    0.14  
## ENTRUE             1.06     0.38     2.81    0.002 
## TCTRUE             -0.14    0.55     -0.25   0.40  
## T_constructionTRUE 0.34     0.36     0.95    0.17  
## landfillTRUE       0.20     0.32     0.62    0.27  
## leakTRUE           0.06     0.24     0.25    0.40  
## garbageTRUE        0.07     0.30     0.25    0.40  
## crackTRUE          1.81     0.34     5.38   0.0000 
## leaning_wallTRUE   2.02     0.55     3.69   0.0001 
## treeTRUE           -0.06    0.24     -0.24   0.41  
## tiltedTRUE         1.19     0.33     3.56   0.0002 
## angleD             0.85     0.49     1.72    0.04  
## angleE             1.30     0.54     2.39    0.01  
## ground_vegTRUE     1.08     0.28     3.87   0.0001 
## scarsTRUE          4.22     0.40     10.53     0   
## conc_rainfallTRUE  2.73     0.76     3.62   0.0001 
## wastewaterTRUE     0.93     0.24     3.88   0.0001 
## bananaTRUE         0.22     0.26     0.85    0.20  
## R1| R2             2.77     1.26     2.19    0.01  
## R2| R3             7.07     1.31     5.41   0.0000 
## R3| R4             12.59    1.44     8.76      0   
## ---------------------------------------------------

par(mfrow=c(5,4))
plot.xmean.ordinaly (risk ~  brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+---------+------------+----------+
## |              |   |N  |y>=1|y>=2     |y>=3        |y>=4      |
## +--------------+---+---+----+---------+------------+----------+
## |brick         |No | 31|Inf | 3.401197| 1.232143681|-0.8938179|
## |              |Yes|498|Inf | 2.285041|-0.072320662|-2.0069620|
## +--------------+---+---+----+---------+------------+----------+
## |wood          |No |451|Inf | 2.199691|-0.164449432|-2.3025851|
## |              |Yes| 78|Inf | 3.637586| 0.998528830|-0.6359888|
## +--------------+---+---+----+---------+------------+----------+
## |mixed         |No |492|Inf | 2.271678|-0.089490571|-2.0126015|
## |              |Yes| 37|Inf | 3.583519| 1.287854288|-0.9932518|
## +--------------+---+---+----+---------+------------+----------+
## |EN            |No |346|Inf | 1.850296|-0.483174092|-2.4693542|
## |              |Yes|183|Inf |      Inf| 0.950976290|-1.2422550|
## +--------------+---+---+----+---------+------------+----------+
## |TC            |No | 24|Inf |      Inf| 1.098612289|-1.0986123|
## |              |Yes|505|Inf | 2.276722|-0.051496526|-1.9664354|
## +--------------+---+---+----+---------+------------+----------+
## |T_construction|No |213|Inf | 1.559566|-0.890145452|-3.1208954|
## |              |Yes|316|Inf | 3.421000| 0.572069249|-1.4715386|
## +--------------+---+---+----+---------+------------+----------+
## |landfill      |No |330|Inf | 1.820333|-0.520534438|-2.6888188|
## |              |Yes|199|Inf | 5.288267| 0.888316880|-1.1737329|
## +--------------+---+---+----+---------+------------+----------+
## |leak          |No |343|Inf | 1.942582|-0.335472736|-2.4203681|
## |              |Yes|186|Inf | 3.817712| 0.621403276|-1.2947272|
## +--------------+---+---+----+---------+------------+----------+
## |garbage       |No |345|Inf | 2.089262|-0.250578322|-2.3154058|
## |              |Yes|184|Inf | 2.967561| 0.464707942|-1.3795147|
## +--------------+---+---+----+---------+------------+----------+
## |crack         |No |435|Inf | 2.134938|-0.386344067|-2.7972813|
## |              |Yes| 94|Inf | 4.532599| 2.685577345|-0.1706255|
## +--------------+---+---+----+---------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.259100|-0.124910650|-2.1684933|
## |              |Yes| 32|Inf |      Inf| 3.433987204| 0.1251631|
## +--------------+---+---+----+---------+------------+----------+
## |tree          |No |206|Inf | 1.693319|-0.536801110|-2.1758334|
## |              |Yes|323|Inf | 3.022050| 0.331167184|-1.7702533|
## +--------------+---+---+----+---------+------------+----------+
## |tilted        |No |436|Inf | 2.113432|-0.399734433|-2.5698999|
## |              |Yes| 93|Inf |      Inf| 3.102342009|-0.4144338|
## +--------------+---+---+----+---------+------------+----------+
## |angle         |C  | 32|Inf |      Inf|-0.251314428|-3.4339872|
## |              |D  |119|Inf | 4.770685| 1.000172216|-1.2259517|
## |              |E  |378|Inf | 1.976494|-0.276887827|-2.1341664|
## +--------------+---+---+----+---------+------------+----------+
## |ground_veg    |No |156|Inf | 1.203973|-1.519825754|-2.9177707|
## |              |Yes|373|Inf | 3.493749| 0.543850879|-1.6518586|
## +--------------+---+---+----+---------+------------+----------+
## |scars         |No |327|Inf | 1.784642|-1.337320357|-4.6821312|
## |              |Yes|202|Inf |      Inf| 3.486355190|-0.7455937|
## +--------------+---+---+----+---------+------------+----------+
## |conc_rainfall |No | 12|Inf |-1.098612|-2.397895273|      -Inf|
## |              |Yes|517|Inf | 2.534114| 0.034819765|-1.8875152|
## +--------------+---+---+----+---------+------------+----------+
## |wastewater    |No |204|Inf | 1.574551|-0.417735201|-3.0757750|
## |              |Yes|325|Inf | 3.261297| 0.253659095|-1.5059589|
## +--------------+---+---+----+---------+------------+----------+
## |banana        |No |348|Inf | 1.932838|-0.383958903|-2.1908551|
## |              |Yes|181|Inf | 4.083171| 0.751741345|-1.5007047|
## +--------------+---+---+----+---------+------------+----------+
## |Overall       |   |529|Inf | 2.327797|-0.003780723|-1.9138903|
## +--------------+---+---+----+---------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

Predicion on test data Eq 1: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel1 <- predict(eq_OLR_01, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_01, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel1)

##     predictedLevel1
##      R1 R2 R3 R4
##   R1  5 12  2  0
##   R2  8 75 10  0
##   R3  0 14 59 11
##   R4  0  1 18  9

p1 <- mean(as.character(test.data$risk) != as.character(predictedLevel1)) #misclassification error
p1

## [1] 0.3392857

Predicion on test data Eq 2: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel2 <- predict(eq_OLR_02, test.data) # predict the levels directly

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel2)

##     predictedLevel2
##      R1 R2 R3 R4
##   R1  6 11  2  0
##   R2  8 74 11  0
##   R3  0 13 60 11
##   R4  0  0 17 11

p2 <- mean(as.character(test.data$risk) != as.character(predictedLevel2))
p2

## [1] 0.3258929

Predicion on test data Eq 3: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel3 <- predict(eq_OLR_03, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_03, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel3)

##     predictedLevel3
##      R1 R2 R3 R4
##   R1  7 10  2  0
##   R2 10 75  8  0
##   R3  0 16 58 10
##   R4  0  0 18 10

p3 <- mean(as.character(test.data$risk) != as.character(predictedLevel3))
p3

## [1] 0.3303571

Predicion on test data Eq 4: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel4 <- predict(eq_OLR_04, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_04, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel4)

##     predictedLevel4
##      R1 R2 R3 R4
##   R1  7 10  2  0
##   R2 10 75  8  0
##   R3  0 15 59 10
##   R4  0  0 18 10

p4 <- mean(as.character(test.data$risk) != as.character(predictedLevel4))
p4

## [1] 0.3258929

Predicion on test data Eq 5: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel5 <- predict(eq_OLR_05, test.data) # predict the levels directly

predictedScores5 <- predict(eq_OLR_05, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel5)

##     predictedLevel5
##      R1 R2 R3 R4
##   R1  5 12  2  0
##   R2  6 76 11  0
##   R3  0 13 62  9
##   R4  0  1 17 10

p5 <- mean(as.character(test.data$risk) != as.character(predictedLevel5))
p5

## [1] 0.3169643

Predicion on test data Eq 6: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel6 <- predict(eq_OLR_06, test.data) # predict the levels directly

predictedScores6 <- predict(eq_OLR_06, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel6)

##     predictedLevel6
##      R1 R2 R3 R4
##   R1  5 12  2  0
##   R2  7 76 10  0
##   R3  0 16 58 10
##   R4  0  1 17 10

p6 <- mean(as.character(test.data$risk) != as.character(predictedLevel6))
p6

## [1] 0.3348214

Predicion on test data Eq 7: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

#Table 

df2 <- data.frame(
  
  "Equations"=c(1:6), 
  "Predicted"=c(1-p1, 
                1-p2,
                1-p3,
                1-p4,
                1-p5,
                1-p6
               
              
    
    
  )
  
  
  
)

df2

##   Equations Predicted
## 1         1 0.6607143
## 2         2 0.6741071
## 3         3 0.6696429
## 4         4 0.6741071
## 5         5 0.6830357
## 6         6 0.6651786

Diagnostic - one model

Erica Goto

2/10/2019

Library & Dataset

Inspect Dataset Using Training and Validation

OLR Equations

Inspect Dataset Using Training and Validation

Equation 1

Creating function with four level

Equation 2

Equation 3

Equation 4

Equation 5 - Based on Equation 1

OLR Equation 6

Predicion on test data Eq 1: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 2: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 3: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 4: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 5: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 6: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 7: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html